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Space Stocks Shine. Momentus up over 70%; Redwire up 17%; Intuitive Machines up 14%; AST SpaceMobile up 11%; Planet Labs up 9%; Virgin Galatic up over 7%
Space stocks shined on Tuesday. Momentus up over 70%; Redwire up 17%; Intuitive Machines up 14%; AST SpaceMobile up 11%; Planet Labs up 9%; Virgin Galatic up over 7%.Tiger News
Tue, May 26
Nasdaq Opens 1% Higher While S&P Jumps 0.6% as AI Optimism Outweighs Middle East Risks
Stocks rose on Tuesday as traders weighed the prospects of a potential U.S.-Iran deal being reached to end the war.Dow Jones Industrial Average popped 258 points, or 0.5%. S&P 500 gained 0.7%, and Nasdaq advanced 1.2%.Semiconductor stocks, which have surged on AI-driven demand, led gains on Tuesday....Tiger News
Tue, May 26
Storage Shares Continue to Rally. Micron Stock Surges Nearly 8% as UBS Sees More Than 100% Upside; DRAM up over 7%; SanDisk, Western Digital up over 3%
Storage shares continued to rally in premarket trading. Micron stock surges nearly 8% as UBS sees more than 100% upside from here; DRAM up over 7%; SanDisk, Western Digital, Rambus up around 3%; Seagate Tech up over 2%.Micron Technology shares have jumped 7.81% in premarket trading to $809 after UBS...Tiger News
Tue, May 26
Oklo Soars 12% on US Energy Department Selection for Plutonium Fuel Talks
Oklo experienced a significant pre-market surge of 12% on Tuesday. The advanced fission power plant developer saw its shares rally in early trading following a major business development announcement.The sharp upward movement appears to be directly linked to company-specific news. Oklo announced it ...Tiger News
Tue, May 26
Iran's Foreign Ministry Says U.S. Has Violated The Ceasefire In The Hormozgan Area, Iran Will Respond And Will Not Hesitate In Defending Itself
Iran's Foreign Ministry Says U.S. Has Violated The Ceasefire In The Hormozgan AreaIran's Foreign Ministry Says Iran Will Respond And Will Not Hesitate In Defending ItselfTiger News
Tue, May 26
Top 10 HK Stock Gainers (5.26)
Hang Seng Index has fell 0.03%, with a year-to-date fell 0.1%; Hang Seng TECH Index has rose 1.59%, with a year-to-date fell 10.3%.TRIGIANT(01300) rose 43.14%, with a trading turnover 135.9 Million HKD, and a year-to-date rose 630%.C.BANNER(01028) rose 32.32%, with a trading turnover 19.3 Million HK...Longport
Tue, May 26
Top 5 HK Stock Gainers/Losers (>100B) (5.26)
Hang Seng Index has fell 0.03%, with a year-to-date fell 0.1%; Hang Seng TECH Index has rose 1.59%, with a year-to-date fell 10.3%.LENOVO GROUP(00992) rose 15.49%, with a trading turnover 9.9 Billion HKD, and a year-to-date rose 96.4%.HUA HONG SEMI(01347) rose 10.45%, with a trading turnover 11.6 Bi...Longport
Tue, May 26
Top 10 HK Stock Losers (5.26)
Hang Seng Index has fell 0.03%, with a year-to-date fell 0.1%; Hang Seng TECH Index has rose 1.59%, with a year-to-date fell 10.3%.GOFINTECH QUANT(00290) fell 23.99%, with a trading turnover 200.1 Million HKD, and a year-to-date rose 138.6%.LIFETECH SCI(01302) fell 23.38%, with a trading turnover 46...Longport
Tue, May 26
HK Grey Market | Beijing's DeepZero Jumps 78%; ViewTrix Tech Rockets 30%; Shenzhen's SDMC Technology Soars 28% as Tech Firms Add to Hong Kong Listing Wave
Viewtrix Technology rose % in Hong Kong’s grey market trading, while SDMC Technology shares rose %; DeepZero gained %.These three Chinese technology companies are eyeing listings in Hong Kong that could raise a combined $285 million, adding to the wave of offerings in the Asian financial hub as firm...Tiger News
Tue, May 26
Premarket Movers | Chip, Storage Stocks Surge with UMC up 11%; Micron up Nearly 4%; AMD, SanDisk up Around 2%; Space Stocks Rally with Redwire up Nearly 12%; LUNR up over 7%
Chip, storage stocks rallied in premarket trading. UMC up over 11%; Navitas, SOXL up around 6%; Micron up nearly 4%; AMD, SanDisk up around 2%.Space stocks gained in premarket trading. Redwire up nearly 12%; Intuitive Machines up over 7%; VCX, AST SpaceMobil up around 6%.Tiger News
Tue, May 26
Preparing the Market: What MoTA Is Meant to Solve?
(26 May 2026, Hong Kong) The market has become comfortable with a simple story about AI in investing: more intelligence, delivered faster.It is a compelling story, but not yet a sufficient one.What most investment technology still fails to solve is not the lack of information, but the lack of structure. Retail investors today have access to more tools, more commentary, and more data than ever before. They can scan markets in real time, summarize disclosures instantly, and ask AI to explain almost any financial development. Yet better access has not automatically translated into better decision-making.That gap is precisely where MoTA enters the conversation.To understand what MoTA is meant to solve, it helps to start with a basic truth: individual investors are not simply competing on insight. They are competing against better-organized decision systems.Professional firms typically do not outperform because they possess a magical source of information. They outperform because their decisions are shaped through structure — through teams, workflows, review layers, risk functions, and role clarity. In other words, they do not merely think harder. They think through systems.Most individuals do not have that advantage. Their process is often improvised across disconnected tools, fragmented inputs, and shifting emotional conditions. Research may be strong, but risk discipline may be weak. Conviction may be high, but process may be inconsistent. Signals may be plentiful, but integration is often poor.This is the problem MoTA appears to be designed to address.Rather than introducing AI as another source of answers, MoTA frames AI as part of a human-AI collaborative investment system. That means the objective is not simply to help a user ask better questions. It is to help a user operate through a better decision architecture.In practical terms, the model is closer to managing an AI investment team than using a conventional AI assistant. Different agents can take on different roles. Workflows can be structured. Responsibilities can be separated. Risk can be built into the process rather than appended at the end. The system is intended not to concentrate judgment into one black box, but to distribute it across a more transparent framework.This matters because the next phase of AI adoption in investing will likely be constrained less by raw model capability than by trust, usability, and control. Investors may be impressed by AI-generated output, but they will hesitate if they cannot understand how a conclusion was formed, where risk was checked, or who ultimately remains accountable for action.MoTA’s relevance, then, is not only that it uses AI. It is that it attempts to organize AI in a way that addresses the practical weaknesses of individual investing: fragmentation, inconsistency, poor process discipline, and insufficient risk structure.That also helps explain why the product should not be reduced to the language of “AI stock picking.” Such language understates the ambition and misstates the problem. MoTA is not meant to solve a narrow recommendation gap. It is meant to solve a process gap.It is meant to make investment decision-making more structured.It is meant to make collaboration between human judgment and machine intelligence more practical.It is meant to make AI participation more controllable.And it is meant to make the investor feel less dependent on opaque output and more supported by a visible operating framework.This is a timely proposition. As AI products proliferate, the market is moving toward a more demanding standard. It will not be enough for platforms to be impressive. They will also need to be governable. They will need to help users not only move faster, but decide better. And they will need to show that more automation does not have to mean less control.The launch of MoTA also reflects the direction Waton Financial (WTF.US) has been moving toward over the past year.Since listing on NASDAQ in 2025, the company has taken a different path from many AI finance platforms rushing to launch new “AI trading features.” Instead, Waton has focused on a bigger question: as AI becomes more common in finance, the real challenge is not just building smarter models, but creating a long-term system where AI and human investors can work together in a way that is regulated, clear, and manageable.Against that backdrop, MoTA — short for Manager of Trading Agents — is meant to be more than just another AI product.More broadly, it reflects Waton’s view of what the next generation of AI investing platforms could look like.Based on the information released so far, MoTA does not follow the familiar “AI makes money for you” narrative that has become common across the market. Instead of replacing investors, the platform is designed around collaboration between AI and humans. AI handles research, analysis, and information processing, while the final investment decision still stays with the investor.At the center of the platform is a multi-agent system, where different AI agents take on different tasks across research, analysis, risk management, and execution. The idea is to organize the investment process in a way that feels closer to how institutional investment teams operate.In many ways, that may be the clearest difference between MoTA and much of today’s AI investing market.What it is trying to solve is not simply how to generate smarter trading ideas, but how to give individual investors a more structured way to make decisions — something closer to the discipline traditionally seen at institutional firms.And behind that shift is a broader change happening across AI investing itself. The conversation is slowly moving away from whether AI can give answers, and more toward how AI fits into the decision-making process — and whether people can actually understand it, manage it, and trust it.If Waton can make that case, MoTA may resonate for reasons that go well beyond novelty. It would speak to one of the central tensions in modern investing: individuals now have access to institutional-grade information flows, but not yet to institutional-grade decision structure.What MoTA is meant to solve is that mismatch.And if that framing gains traction, the market may begin to look at AI investing platforms differently — not as tools that merely generate answers, but as systems that shape how answers are produced, tested, and trusted.Media Contact:Email: ir@watonfinancial.comWebsite: https://wtf.usDisclaimer: This press release contains forward-looking statements. Actual results may differ materially from those expressed or implied. This is not investment advice. Past performance does not guarantee future results.26/05/2026 Dissemination of a Financial Press Release, transmitted by EQS News.The issuer is solely responsible for the content of this announcement.Media archive at www.todayir.comEQS
Tue, May 26
Xiaomi: Doubting After the Selloff? The Worst Is Over
Xiaomi Group (1810.HK) released its Q1 2026 results (through Mar 2026) after Hong Kong market close on May 26, Beijing time. The key points are as follows:1. Overall results: revenue RMB 99.1bn, down 11% YoY. The decline was driven by smartphones and IoT, with legacy hardware (smartphones x AIoT) re...Dolphin Research
Tue, May 26
MNSO: AI Spend Turbocharged, But Still Buying Growth
On the afternoon of May 26 Beijing time, MINISO reported 1Q26 results. Stripping out the headline boost from AI investment gains, the core issue remains: revenue is running ahead of profit growth. $Miniso(MNSO.US) $MNSO(09896.HK)1) Revenue still strong, China beat. MINISO delivered revenue of RMB 5....Dolphin Research
Tue, May 26
Crypto Daily｜Ethereum Foundation Will Shrink, Sell Less ETH, and Focus on 'CROPS'; Bitcoin, Crypto Prices Tick up as US-Iran Peace Deal Odds Climb
Crypto Daily is our column tracking crypto market trends, offering timely insights and valuable updates to keep you informed.Crypto NewsButerin says Ethereum Foundation will shrink, sell less ETH, and focus on 'CROPS'thereum co-founder Vitalik Buterin said the Ethereum Foundation will choose "longev...Tiger News
Tue, May 26
Xunce Launches TokenONE, the World’s First TokenOS Operating System, Igniting the 'Token Factory' Industrial Revolution
At a pivotal moment when AI technology is shifting from model competition to industrial application, Xunce (3317.HK) officially launched TokenONE, the world’s first TokenOS operating system, on May 25,2026.Centered on the Token as the core asset unit, TokenONE establishes an industrialized production system that transforms raw data into high-value scenario-specific Tokens. It directly tackles the current bottleneck hindering AI adoption – the scarcity of enterprise-grade scenario data – and provides a solution to the toughest "last mile" challenge of large-scale AI deployment, sparking an industrial revolution modeled on the "Token Factory".A New Operating System for the AI Era: Ten Key Product Differentiators Build a Strong MoatAs a next-generation operating system for the native AI era – following Windows in the PC age and iOS/Android in the mobile internet era – TokenONE empowers enterprise data to be directly invoked by AI models as data Tokens. Every model invocation generates quantifiable and traceable business value and insights, driving large-scale commercial adoption of Token Factories across industries.Throughout the entire chain of data refining, transmission, decision‑making, and metering, TokenONE transforms each model invocation and application from mere compute consumption into measurable, traceable, and optimizable business value. Every Token drives business decisions directly, making large model outputs quantifiable.With ten distinct technological and product advantages, TokenONE builds the core capabilities of a Token Factory that closes the loop from data to value.Turning "Dormant Data" into "High‑Energy AI Fuel" – Igniting the Token Factory Industrial RevolutionWhile today’s large models boast massive parameter counts, they suffer from an acute shortage of enterprise‑grade scenario data. For example, in finance, risk control logic is embedded in transaction records and risk reports; in manufacturing, process know‑how is locked in equipment logs and quality inspection documents; in healthcare, diagnostic expertise is scattered across imaging files and medical record systems. The vast amounts of high‑quality data, industry knowledge, and scenario experience accumulated by enterprises remain "dormant" and cannot be effectively accessed by AI.TokenONE precisely addresses this pain point. It converts otherwise non‑standard and unusable scenario knowledge into standardized, tradable, and auditable scenario Tokens – a process of data Tokenization. This turns dormant data into high‑energy AI production materials and makes large‑scale industrialization of scenario Tokens a reality.On the "industrialized" output side of the Token Factory, TokenONE establishes nine standardized processing stages and five core workflows, enabling end‑to‑end industrialized production of raw data – from "material entry" to "value realization". It upgrades data processing from a "handicraft workshop" model into a replicable, scalable, and auditable mass‑production system.Foundation Layer – Standardized Material Entry. TokenONE connects multi‑source heterogeneous data from inside and outside the enterprise. Its intelligent cleaning and standardization engine converts "messy and dirty" raw data into uniformly specified "industrial raw materials".Middle Layer – Core Refinery. Standardized data enters the Token Factory’s core production line. The real‑time computing engine completes deep processing at millisecond speeds, performs precise mapping using vertical scenario labels, and finally packages the results into measurable, pricable, and exchangeable scenario Tokens.Application & Frontier Layer – Value Realization. Packaged scenario Tokens are precisely injected into various AI agents. Through model tuning modules, they empower large models and intelligent hardware, directly driving business decisions. Every downstream call is a value realization event.Metering Layer – The World’s First Pay‑per‑Invocation Operating System. From hardware isolation to system‑level attestation, TokenONE ensures tamper‑proof and fully transparent billing, enabling security, compliance, governance, and auditing. It shatters the industry’s "black box" of billing, helping enterprises identify inefficient or wasteful consumption, and transforms AI spending from a passive cost into an actively managed, controllable asset.An Industrial Enabler for Large‑Scale AI Deployment: Building Scenario Token Factories Across IndustriesThe AI industry stands at a critical inflection point, transitioning from the first half – a race for model parameters – to the second half, where real value realization takes center stage. The bridge enabling this shift is the Tokenization and industrialized supply of scenario data. As an industry‑scale bridge, TokenONE provides customers with ready‑to‑use, standardized scenario Tokens, lowering the barriers to AI adoption. It also supplies large model providers with massive volumes of vertical domain data, systematically solving the pain point of scenario data scarcity. Moreover, it establishes a benchmark for "Token production" across the entire AI industry, accelerating AI’s journey from labs to every sector and ensuring that AI delivers tangible value.Looking ahead, Xunce will continue to build on the TokenONE architecture and co‑establish vertical scenario Token Factories with industry leaders, covering high‑value fields such as healthcare, high‑end manufacturing, finance, and energy & power. This will embed the industrial capability of data Tokenization into every critical industry, forming a new type of infrastructure network that deeply integrates AI with the real economy.Business Model: From Data Governance to a Closed‑Loop Token EconomyTo put the commercial essence of TokenONE more directly: it is essentially a "Tokenized upgrade" of Palantir’s Ontology – or Ontology 2.0. Palantir’s Ontology breaks down government and enterprise data silos to enable data‑driven decision intelligence, but data itself remains a "static asset". TokenONE goes a step further: by encapsulating data into Tokens, it endows data with measurable, pricable, and tradable economic attributes, transforming data from "static assets" into "dynamic production materials". While Ontology 1.0 solves "how data can be understood", TokenONE solves how data can be industrially produced and monetized.This commercial core is externalized into two pricing paths that grow in sync with customers’ maturity:Pay‑per‑Token Metering – Lowering the decision threshold for enterprise AI adoption. Billing is based on actual Token consumption, settled monthly or quarterly – pay for what you use. This "verify‑first, invest‑later" design ensures that the value of the AI system grows in step with the customer’s actual usage.Full Buyout – Once an enterprise has fully validated the business value of the AI system and has a clear expectation of long‑term use, it can seamlessly upgrade to a buyout model, acquiring full system ownership and absolute data sovereignty. Historical pay‑per‑Token payments can be credited toward the buyout price proportionally, fully protecting the customer’s prior investment.The pricing logic also departs from the old "compute‑stacking" framework, instead building value around the Token’s business impact: the per‑invocation price depends on data scarcity, real‑time requirements, and industry complexity; invocation volume reflects actual usage depth in real business processes; and module depth measures how deeply the system is embedded into the customer’s workflows – the more access points and the deeper the integration, the higher the overall value.Market data is already validating the explosive potential of this model. In April 2026, Xunce’s annualized recurring revenue (ARR) from Token data invocation grew 300% quarter‑on‑quarter. Token‑based pricing currently accounts for approximately 5% of revenue, with a target to raise that to 20%–30% by the end of 2026. Even more striking is the pricing power: Xunce’s vertical‑domain Token pricing ranges from USD10-100 per million Tokens – more than ten times that of general‑purpose large models – and continues to rise with greater scenario specificity. This indicates that Xunce is undergoing a systemic shift from traditional subscription models to Token‑based metering and value‑sharing models, with the Token business becoming a powerful new growth engine.ConclusionThe AI industry is currently at a turning point – moving from "lab invention" to "real‑world commercial value realization". Large model providers are obsessively racing for more parameters, but a growing consensus recognizes that models without scenario data are engines without fuel.Just as iOS and Android unified the underlying logic of the mobile internet, Xunce’s TokenONE is defining the underlying rules of the AI era – starting from real‑world industry scenarios, connecting technology and resources through data, and making large‑scale deployment of scenario Token Factories a reality. As more vertical industry Token Factories come online, Xunce is poised to become a core platform with phenomenal influence in the AI era and a leading driver of AI deployment.26/05/2026 Dissemination of a Financial Press Release, transmitted by EQS News.The issuer is solely responsible for the content of this announcement.Media archive at www.todayir.comEQS
Tue, May 26
Xunce (03317.HK) Unveils TokenOS Operating System TokenONE, Ushering in a New Era of Vertical Token Factories
While the global AI industry remains mired in an arms race over parameter scales and compute clusters, a deeper structural contradiction is surfacing, large language models have no shortage of engines, yet they face a critical shortage of the “fuel” needed to power those engines at peak performance. The true inflection point of this competition has quietly shifted from “building engines” to “refining fuel”.TokenONE: A New AI-Native Operating System Built Around Token EconomicsWithout high-octane fuel, even the best engine runs inefficiently. Over the past two years, the AI industry has frantically scaled up model parameters and expanded compute clusters. Yet when these models enter the core operations of enterprises, the limitations of generic Tokens become starkly apparent — they function like low-grade gasoline: abundant in volume but low in energy density. Enterprises attempting to solve specialized problems with generic Tokens often require repeated iterations, resulting in massive amounts of wasted compute.What enterprises need is not a metering device that charges “by the word,” but Specialized Tokens that can hit business decisions on the first try. Specialized Tokens are precisely this “high-octane fuel” — refined from industry-specific private data through cleansing, standardization, alignment, and knowledge augmentation. A single Specialized Token carries information density and business logic equivalent to hundreds of generic Tokens stacked together.Xunce (03317.HK) has unveiled the world’s first TokenOS operating system — TokenONE. With “refinement, delivery, and decision-making” as its core capabilities, TokenONE transforms raw data into high-purity, high-value “Specialized Tokens” through an industrialized process, enabling direct consumption by various models and AI Agents while making LLM outputs measurable and auditable.An End-to-End Industrial PipelineTokenONE operates around a complete industrial assembly line. It begins by addressing the “raw material intake” challenge — through its data tokenization capability, it converts enterprises’ fragmented, heterogeneous, non-standard private data into measurable, priceable, and exchangeable industrial raw materials. Whether financial risk-control logs, manufacturing equipment records, or medical imaging archives, TokenONE delivers precise cleansing, standardization, and tagging.Once raw materials enter the “refinery,” TokenONE’s multi-compute foundation comes into play. Its unifiedly interfaces with GPU, CPU, and NPU resources, supporting hybrid cloud and on-premises deployment. This ensures that core data never leaves the enterprise’s domain while flexibly scheduling computed resources. The real-time compute engine completes deep processing at millisecond speeds, pursuing 100% accuracy in specialized professional scenarios — a critical requirement for zero-tolerance domains such as financial risk control and industrial quality inspection.At the final value-delivery stage, TokenONE is fully LLM-native and not bound to any single model vendor. Enterprises can flexibly switch and orchestrate both general-purpose large models and vertical models on the platform. TokenONE also supports enterprises in rapid training, fine-tuning, and deploying proprietary vertical models and small models — achieving lower costs, faster inference speeds, and fully private on-premise operation. From data ingestion to model invocation, TokenONE covers the full chain and takes responsibility for the final business outcome.The “Ford Moment” for the AI Industry: Vertical Token Factories Go IndustrialFrom a strategic perspective, TokenONE’s significance extends far beyond a technical solution. For the first time, it has defined an industrial production paradigm for “core production materials” in the AI industry — much as Ford’s assembly line transformed automobile manufacturing from artisanal workshops to mass production, and as container standardization turned global trade from fragmented cargo handling into systematized logistics. TokenONE replicates this logic in the AI domain: transforming Tokens from “artisanal” custom processing to industrialized, standardized output.This complete industrial system delivers breakthroughs on three levels:For enterprise clients, TokenONE compresses AI deployment cycles from months to days. Token Factories directly output standardized Specialized Tokens, making AI investments measurable and traceable.For LLM vendors, TokenONE provides scalable vertical data supply, systematically addressing the industry bottleneck of scarce specialized data.For the AI industry at large, TokenONE drives a paradigm shift from “project-based” to “product-based” delivery, and from “custom development” to “standardized supply” — a prerequisite for AI to move from laboratories into production systems.The Second Half Has BegunThe AI industry is undergoing a historic transition from “technology-driven” to “production-driven.” The winners of the first half were companies with the strongest computing and the most parameters. The winners of the second half will be those capable of bringing AI into production systems on a scale, at low cost, and in a measurable way. The launch of TokenONE marks the official beginning of this second half.Built on the underlying architecture of this Token Factory, Xunce is now partnering with leading enterprises across vertical industries to co-build Vertical Token Factories — extending the industrialized capability of data tokenization into every critical sector: finance, healthcare, manufacturing, and energy. Each Vertical Token Factory that comes online represents a material expansion of AI’s application boundary within that industry. When Specialized Tokens become the “standard interface” for AI systems across industries, AI will have truly entered the core of production.While the industry continues debating model parameters, Xunce has already built the “infrastructure” and “power source” of the AI era. From “crude oil” to “high-octane fuel,” from “artisanal workshops” to “industrial production,” TokenONE is writing the underlying logic for the next decade of the AI industry.As a scarce asset in the AI infrastructure space, Xunce’s long-term investment value is accelerating in tandem with the industrialization of Token Factories.26/05/2026 Dissemination of a Financial Press Release, transmitted by EQS News.The issuer is solely responsible for the content of this announcement.Media archive at www.todayir.comEQS
Tue, May 26
HK Movers | XL2CSOPHYNIX Jumps Over 14% as SK Hynix Unveils "IHBM" Thermal Solution to Boost AI Performance
SK hynix Inc. announced today the launch of the iHBM solution that embeds integrated cooling elements(ICEs) within the high-bandwidth memory(HBM) package for next-generation HBM products.Hong Kong-listed XL2CSOPHYNIX jumped more than 14%.Heat management has become a critical challenge as HBM technol...Tiger News
Tue, May 26
HK Movers | Lenovo Stock Jumps 12% to A New High
Lenovo Group's stock surged by 12% in Hong Kong, hitting a new high again, with a 34% spike over two trading days. The company's performance last quarter exceeded expectations across the board, with its AI business becoming a core growth pillar.Tiger News
Tue, May 26
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